Near non-adaptive virtual metrology and chamber control

ABSTRACT

Embodiments of the present invention relate to a method for a near non-adaptive virtual metrology for wafer processing control. In accordance with an embodiment of the present invention, a method for processing control comprises diagnosing a chamber of a processing tool that processes a wafer to identify a key chamber parameter, and controlling the chamber based on the key chamber parameter if the key chamber parameter can be controlled, or compensating a prediction model by changing to a secondary prediction model if the key chamber parameter cannot be sufficiently controlled.

This application is a continuation of U.S. patent application Ser. No.12/766,626, filed on Apr. 23, 2010, and entitled “Near Non-AdaptiveVirtual Metrology and Chamber Control,” which claims the benefit of U.S.Provisional Patent Application Ser. No. 61/224,352, filed on Jul. 9,2009, and entitled “Near Non-Adaptive Virtual Metrology and ChamberControl,” which applications are incorporated herein by reference intheir entireties.

TECHNICAL FIELD

The present invention relates generally to a method for semiconductorwafer processing control, and more particularly to a method for a nearnon-adaptive virtual metrology for wafer processing control.

BACKGROUND

Generally, in semiconductor processing, metrology is the measuring ofdimensions and characteristics of a semiconductor wafer afterprocessing. Virtual metrology (VM) typically uses a model to predict theresulting dimensions and characteristics of a wafer based on parametersfrom the processing chamber.

Adaptive VM generally requires building a model based on metrologyresults from two or three wafers of a lot of wafers and chamberparameters for each tool. This model is then used to predict the outcomeof the processing of the wafers of the following lot. Because thecontrol is adjusted on a per lot basis, the control is considered to belot-to-lot (LtL) control.

However, metrology results for the two or three wafers generally takesbetween a few hours and half a day to receive from the metrology tools.This delay is typically impracticable for current manufacturing needs.Ideally, VM could be used to control processing on a wafer-to-wafer(WtW) basis to gain higher precision and accuracy were it not for thedelay in obtaining metrology results. Also, the conventional methodsgenerally cannot accomplish WtW control because the sampling rate islimited. Further, adaptive VM is not able to identify and correctchamber drift. Thus, conventional adaptive VM methods cannot realize WtWcontrol or correction of chamber drift. Accordingly, there is a need inthe art for a method to overcome the above described shortcomings.

SUMMARY OF THE INVENTION

These and other problems are generally solved or circumvented, andtechnical advantages are generally achieved, by embodiments of thepresent invention.

In accordance with an embodiment of the present invention, a method forprocessing control comprises diagnosing a chamber of a processing toolthat processes a wafer to identify a key chamber parameter, andcontrolling the chamber based on the key chamber parameter if the keychamber parameter can be controlled, or compensating a prediction modelby changing to a secondary prediction model if the key chamber parametercannot be sufficiently controlled.

In accordance with another embodiment of the present invention, a methodfor controlling a process tool comprises predicting a result of a waferprocessed by the chamber and using correlation to determine acoefficient of correlation for a chamber parameter data set to aresidual data set. The chamber parameter data set comprises chamberdata-points each relating to either one of historical wafers or thewafer, and the residual data set comprises error data-points eachrelating to either one of the historical wafers or the wafer. The methodfurther comprises defining a stable range based on each of the errordata-points that meets a target, defining an unstable range based oneach of the error data-points that does not meet the target, analyzing adifference between the chamber parameter stable range and the chamberparameter unstable range, and identifying the key chamber parameter ifthe difference between the chamber parameter stable range and thechamber parameter unstable range is within a second limit, all done whenthe coefficient of correlation is within a first limit. The method alsocomprises controlling the chamber based on the key chamber parameter orcompensating a prediction model by changing to a secondary predictionmodel.

In accordance with yet another embodiment of the present invention, amethod for correcting chamber drift comprises correlating a chamberparameter data set to a residual data set to obtain a coefficient ofcorrelation and identifying a non-key chamber parameter if thecoefficient of determination is less than a first limit. The chamberparameter data set comprises chamber data points each relating to eitherone of historical wafers or the wafer, and the residual data setcomprises error data points each relating to either one of thehistorical wafers or the wafer. The method further comprises defining achamber parameter stable range based on a first set of error data pointsthat are within a target range, defining a chamber parameter unstablerange based on a second set of error data points that are not within thetarget range, analyzing a difference between the chamber parameterstable range and the chamber parameter unstable range, identifying thekey chamber parameter if the difference between the chamber parameterstable range and the chamber parameter unstable range is within a secondlimit, and controlling the chamber based on the key chamber parameter ifthe key chamber parameter is not passive or compensating a predictionmodel by changing to a secondary prediction model if the key chamberparameter is passive.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the present invention, and theadvantages thereof, reference is now made to the following descriptionstaken in conjunction with the accompanying drawing, in which:

FIG. 1 is a process employing near non-adaptive VM in accordance with anembodiment of the present invention;

FIG. 2 is a near non-adaptive VM module in accordance with an embodimentof the present invention;

FIG. 3 is a flowchart of a chamber diagnosis module in accordance withan embodiment of the present invention;

FIG. 4 is an example of stable and unstable data-points determined by anembodiment of the present invention; and

FIG. 5 is a defined stable and unstable range for a chamber parameter asdetermined by an embodiment of the present invention.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

The making and using of the present embodiments are discussed in detailbelow. It should be appreciated, however, that the present inventionprovides many applicable inventive concepts that can be embodied in awide variety of specific contexts. The specific embodiments discussedare merely illustrative of specific ways to make and use the invention,and do not limit the scope of the invention.

The present invention will be described with respect to embodiments in aspecific context, namely a near non-adaptive virtual metrology methodwith chamber control. The invention may also be applied, however, toother processing control methods, such as adaptive virtual metrology.

FIG. 1 illustrates a process employing near non-adaptive VM inaccordance with an embodiment of the invention. A chamber of aprocessing tool 2 performs some processing steps, for example etchingpolysilicon on the wafer. Sensors on the chamber send chamber parameters4 to the near non-adaptive VM module 6. The number of chamber parameterswill vary depending on the processing tool and the process. For example,a polysilicon etching process may comprise approximately one thousandparameters, and a simple etch may comprise approximately one hundredparameters. Example parameters for a simple etching process are opticalemission spectroscopy (OES), radio frequency (RF) power, and the like.In situ metrology tools then take measurements of the wafer while in thechamber 8. The near non-adaptive VM application 6 then is able todetermine key, or important, chamber parameters, as discussed in detailbelow, and uses chamber automatic process control (APC) 10 to controlthose key chamber parameters, i.e. feed-back control, and/or switches toa more appropriate prediction model. Further, the near non-adaptive VMapplication can control subsequent processing tools, i.e. feed-forwardcontrol 12.

FIG. 2 illustrates the near non-adaptive VM module 6 in accordance withan embodiment. A chamber diagnosis module 20 uses the chamber parameters4 and prediction errors to identify key chamber parameters. Once the keychamber parameters are identified, the near non-adaptive VM module 6uses chamber control limit setting module 22 to determine theappropriate action necessary to control the chamber for subsequentprocessing of wafers. However, if a particular key chamber parameter ispassive only and cannot be controlled, such as optical emission sensors(OES), the near non-adaptive VM module 6 will run a switch predictionmodule 24 to determine an appropriate model for the key chamberparameter condition. FIG. 2 illustrates two prediction models,Prediction Model_(n) 26 and Prediction Model_(n+1) 28, however, moremodels may be used for a single passive chamber parameter. Further, achamber is likely to have multiple passive parameters.

FIG. 3 illustrates a flowchart of the chamber diagnosis module 20 inaccordance with an embodiment. The chamber diagnosis module 20 isessentially a statistical analysis of each chamber parameter. First, thewafer result of a currently processed wafer in a chamber is predicted50. The predicted wafer result comprises predicted measurement resultsof the currently processed wafer. Then, a residual is determined for thewafer. The residual is the error of the prediction. The error is thedifference of the predicted results from the in situ metrology resultsreceived by the module after processing the wafer in the chamber.

Further, information from historical, or previously processed, wafers isused during this statistical analysis. This information comprises thepredicted wafer result, the residual, and the value or indication ofeach chamber parameter for each historical wafer. For simplicity andclarity, this information can be organized into data sets. For example,a residual data set comprises the residual of the currently processedwafer and each historical wafer. Further, a first chamber parameter dataset may comprise the value or indication of a first chamber parameterfor the currently processed wafer and each historical wafer, andlikewise for a second chamber parameter data set, and so on. Embodimentscontemplate that any number of historical wafers and correspondinginformation may be used in this process.

Next, the correlation of the residual data set to each chamber parameterdata set is then evaluated 52. If the coefficient of determination, R²,for a particular chamber parameter is less than or equal to a firstthreshold, such as 0.3 in this example 54, then the relationship betweenthe chamber parameter data set and the residual data set is consideredto be weak, and the chamber parameter is not a key chamber parameter 68.In other embodiments, if R² is less than or equal to 0.5, the particularchamber parameter is not a key chamber parameter. Otherwise, the impactof the parameter on the residual must be clarified 56.

Then, the absolute value of each datum of the residual data set iscompared to a target 58. The target is determined on a case-by-casebasis depending partly on the chamber or processing. In someembodiments, the target is between approximately twenty and thirtypercent of the allowable process variation; however, other targets arenot excluded. If the absolute value of a datum of the residual data setis less than the target, the datum is considered to be stable, and thechamber parameter stable range is defined by regression based on allstable data in the residual data set to find a linear expression of therange 60. Otherwise, if the datum of the residual data set is not lessthan the target, the datum is considered to be unstable, and the chamberparameter unstable range is defined by regression based on all unstabledata in the residual data set to find a linear expression of the range62. An example of stable and unstable data of the residual data set isillustrated in FIG. 4. FIG. 4 shows the key chamber parameter range onthe x-axis and the residual range on the y-axis. The stable range isbounded by the range of approximately 1900 to 1915 on the x-axis and therange of approximately −40 to 40 on the y-axis.

Next, referring back to FIG. 3, the difference between the chamberparameter stable range and unstable range is analyzed using an analysisof variance (ANOVA) 64. From this analysis, if the p-value, is less thana second threshold, such as 0.05 in this example 66, then the chamberparameter is considered a key parameter 70, or in other words, theeffect of the parameter on the mean of the residual is statisticallysignificant. Otherwise, the parameter is a non-key parameter 68. Thep-value is a value for the null hypothesis assuming all samples are fromthe same population, or from different populations with the same mean.If the p-value is near zero, this casts doubt on the null hypothesis andsuggests that at least one sample mean is significantly different fromthe other sample means. In other embodiments, the p-value may be othervalues, such as 0.01. FIG. 5 shows a defined stable and unstable rangefor a chamber parameter. The variance for each range is shown by thedashed lines. As can be seen, the stable range is much smaller than theunstable range, as is the variance.

In this embodiment, a prediction model would need to be built, at most,on a day-to-day frequency, or less frequently, rather than on alot-to-lot basis. By utilizing this method, advantages over the priorart may be realized. This allows real time WtW control feasibilitywithout limitation on metrology measurement data feedback rate. Further,the chamber condition is detected and controlled typically causinggreater prediction accuracy. Any chamber drift or aging or chamberbehavior shift as a result of a periodic maintenance (PM) event may becompensated by controlling the chamber conditions or compensating orswitching the prediction models. For example, the method may allow forforty percent improvement in the prediction of a trench depth etchingprocess and for ten percent improvement in the prediction of apolysilicon critical dimension (CD) etching process. Next, the methodallows for the realization of virtual wafer acceptance testing (VWAT)and not only single process prediction. VWAT may be realized bypredicting the final processing results of each wafer while each waferis still being processed, even in early processing steps.

Although the present invention and its advantages have been described indetail, it should be understood that various changes, substitutions andalterations can be made herein without departing from the spirit andscope of the invention as defined by the appended claims. Moreover, thescope of the present application is not intended to be limited to theparticular embodiments of the process, machine, manufacture, compositionof matter, means, methods and steps described in the specification. Asone of ordinary skill in the art will readily appreciate from thedisclosure of the present invention, processes, machines, manufacture,compositions of matter, means, methods, or steps, presently existing orlater to be developed, that perform substantially the same function orachieve substantially the same result as the corresponding embodimentsdescribed herein may be utilized according to the present invention.Accordingly, the appended claims are intended to include within theirscope such processes, machines, manufacture, compositions of matter,means, methods, or steps.

What is claimed is:
 1. A method comprising: processing a first wafer ina chamber of a processing tool; identifying a key chamber parameter ofthe chamber, the identifying comprising using a difference between apredicted value of the first wafer according to a prediction model andan actual value of the first wafer as a result of the processing thefirst wafer in the chamber, a first chamber parameter for the actualvalue of the first wafer being part of a first chamber parameter dataset, the difference being part of a residual data set, the key chamberparameter identified in response to a coefficient of determinationbetween the first chamber parameter data set and the residual data setbeing greater than a first threshold; and during processing a secondwafer in the chamber, reducing a difference between a predicted value ofthe second wafer according to the prediction model and an actual valueof the second wafer as a result of the processing the second wafer inthe chamber by controlling the chamber based on the key chamberparameter, the predicted value of the second wafer corresponding in typeto the predicted value of the first wafer, the actual value of thesecond wafer corresponding in type to the actual value of the firstwafer, the processing the second wafer being after the processing thefirst wafer.
 2. The method of claim 1, further comprising controlling asubsequent chamber of a subsequent processing tool during processing thefirst wafer in the subsequent chamber.
 3. The method of claim 1, whereinthe identifying the key chamber parameter of the chamber comprises astatistical analysis of a plurality of chamber parameters to determinewhich of the plurality of chamber parameters most closely corresponds toa variation of the chamber processing.
 4. The method of claim 1, whereinthe identifying the key chamber parameter of the chamber comprises:correlating the first chamber parameter data set to the residual dataset, wherein the first chamber parameter data set further compriseschamber data points each corresponding to a historical wafer or thefirst wafer, and wherein the residual data set further comprises errordata points each corresponding to the historical wafer or the firstwafer, each of the error data points comprising a difference between apredicted value of a respective wafer and an actual value of therespective wafer; when the correlating indicates a strong correlation,defining a stable range and an unstable range based on each of the errordata points in relation to a target; analyzing a difference between thestable range and the unstable range; and identifying the key chamberparameter if the difference between the stable range and the unstablerange is within a limit.
 5. The method of claim 4, wherein the target isbetween twenty and thirty percent of an allowable process variation. 6.The method of claim 4, wherein the defining the stable range and theunstable range each includes using regression analysis.
 7. The method ofclaim 4, wherein the analyzing the difference includes using an analysisof variance.
 8. The method of claim 1, wherein the first threshold is0.3.
 9. A method comprising: processing a first wafer in a chamber of aprocessing tool; identifying a key chamber parameter of the chamber, theidentifying comprising using a difference between a predicted value ofthe first wafer according to a prediction model and an actual value ofthe first wafer as a result of the processing the first wafer in thechamber, the identifying further comprising: correlating a chamberparameter data set to a residual data set, wherein the chamber parameterdata set comprises chamber data points each corresponding to ahistorical wafer or the first wafer, and wherein the residual data setcomprises error data points each corresponding to the historical waferor the first wafer, each of the error data points comprising adifference between a predicted value of a respective wafer and an actualvalue of the respective wafer; when the correlating indicates a strongcorrelation, defining a stable range and an unstable range based on eachof the error data points in relation to a target; analyzing a differencebetween the stable range and the unstable range; and identifying the keychamber parameter if the difference between the stable range and theunstable range is within a limit; and during processing a second waferin the chamber, reducing a difference between a predicted value of thesecond wafer according to the prediction model and an actual value ofthe second wafer as a result of the processing the second wafer in thechamber by controlling the chamber based on the key chamber parameter,the predicted value of the second wafer corresponding in type to thepredicted value of the first wafer, the actual value of the second wafercorresponding in type to the actual value of the first wafer, theprocessing the second wafer being after the processing the first wafer.10. The method of claim 9, further comprising controlling a subsequentchamber of a subsequent processing tool during processing the firstwafer in the subsequent chamber.
 11. The method of claim 9, wherein theidentifying the key chamber parameter of the chamber further comprises astatistical analysis of a plurality of chamber parameters to determinewhich of the plurality of chamber parameters most closely corresponds toa variation of the chamber processing.
 12. The method of claim 9,wherein the target is between twenty and thirty percent of an allowableprocess variation.
 13. The method of claim 9, wherein the defining thestable range and the unstable range each includes using regressionanalysis.
 14. The method of claim 9, wherein the analyzing thedifference includes using an analysis of variance.
 15. A methodcomprising: processing a first wafer in a chamber of a processing tool;identifying a key chamber parameter of the chamber, the identifyingcomprising using a difference between a predicted value of the firstwafer according to a first prediction model and an actual value of thefirst wafer as a result of the processing the first wafer in thechamber, a first chamber parameter for the actual value of the firstwafer being part of a first chamber parameter data set, the differencebeing part of a residual data set, the key chamber parameter identifiedin response to a coefficient of determination between the first chamberparameter data set and the residual data set being greater than a firstthreshold; during processing a second wafer in the chamber, reducing adifference between a predicted value of the second wafer according tothe first prediction model and an actual value of the second wafer as aresult of the processing the second wafer in the chamber by controllingthe chamber based on the key chamber parameter, the predicted value ofthe second wafer corresponding in type to the predicted value of thefirst wafer, the actual value of the second wafer corresponding in typeto the actual value of the first wafer, the processing the second waferbeing after the processing the first wafer; and in accordance with thekey chamber parameter, switching the processing tool from the firstprediction model to a second prediction model.
 16. The method of claim15, wherein the identifying the key chamber parameter of the chambercomprises: correlating the first chamber parameter data set to theresidual data set, wherein the first chamber parameter data set furthercomprises chamber data points each corresponding to a historical waferor the first wafer, and wherein the residual data set further compriseserror data points each corresponding to the historical wafer or thefirst wafer, each of the error data points comprising a differencebetween a predicted value of a respective wafer and an actual value ofthe respective wafer; when the correlating indicates a strongcorrelation, defining a stable range and an unstable range based on eachof the error data points in relation to a target; analyzing a differencebetween the stable range and the unstable range; and identifying the keychamber parameter if the difference between the stable range and theunstable range is within a limit.
 17. The method of claim 16, whereinthe analyzing the difference includes using an analysis of variance. 18.The method of claim 17, wherein the analyzing the difference furtherincludes determining a p-value for the analysis of variance, and whereinthe difference between the stable range and the unstable range is withinthe limit when the p-value is less than a second threshold.
 19. Themethod of claim 18, wherein the second threshold is 0.05.
 20. The methodof claim 18, wherein the first threshold is 0.3.